Based on SwiftDocs
Let's do this in a practical way...
Classes and Structures both can store properties:
As you can see in the image, there's one first difference: a class need an explicit init method. So, we have to do this:
Based on SwiftDocs
Let's do this in a practical way...
Classes and Structures both can store properties:
As you can see in the image, there's one first difference: a class need an explicit init method. So, we have to do this:
Title: The Smol Training Playbook: The Secrets to Building World-Class LLMs
URL Source: https://huggingfacetb-smol-training-playbook.hf.space/
Published Time: Oct. 30, 2025
Markdown Content: Table of Contents
Table of Contents
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Before we look at some common commands, I just want to note a few keyboard commands that are very helpful:
Up Arrow: Will show your last commandDown Arrow: Will show your next commandTab: Will auto-complete your commandCtrl + L: Will clear the screen| ### | |
| ### [2023-06-19] UPDATE: Just tried to use my instructions again on a fresh install and it failed in a number of places. | |
| ###. Not sure if I'll update this gist (though I realise it seems to still have some traffic), but here's a list of | |
| ###. things to watch out for: | |
| ### - Check out the `nix-darwin` instructions, as they have changed. | |
| ### - There's a home manager gotcha https://github.com/nix-community/home-manager/issues/4026 | |
| ### | |
| # I found some good resources but they seem to do a bit too much (maybe from a time when there were more bugs). | |
| # So here's a minimal Gist which worked for me as an install on a new M1 Pro. |
| Meta (Instagram, Facebook) | |
| // Узлы | |
| 157.240.253.174, 157.240.253.172, 157.240.253.167, 157.240.253.63, 157.240.253.32 | |
| 157.240.252.174, 157.240.252.172, 157.240.252.167, 157.240.252.63, 157.240.252.38 | |
| 57.144.112.34, 57.144.110.1, 157.240.205.174, 87.245.223.97 | |
| // Подсети | |
| 213.102.128.0/24 | |
| 204.15.20.0/22 | |
| 199.201.0.0/16 |
java.util.Random, An instance of this class is used to generate a stream of pseudorandom numbers. The class uses a 48-bit seed, which is modified using a linear congruential formula. (See Donald Knuth, The Art of Computer Programming, Volume 2, Section 3.2.1.)
This class has 6 nifty functions:
nextFloat()